?? Day 164 of 365: Tuning for Imbalanced Datasets ??
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey, Analyst!
Welcome to Day 164 of our #365DaysOfDataScience journey! ??
Imbalanced data is everywhere, from fraud detection to medical diagnosis, and tuning for it can make a huge difference in how well our models perform. Let’s dive in and experiment with different strategies!
?? What We’ll Be Doing Today:
??- Handling imbalanced datasets with techniques like SMOTE, class weighting, and oversampling/undersampling.
??- Understanding the impact of imbalance on model evaluation and tuning.
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?? Learning Resources:
??- Read: Articles on handling imbalanced data in machine learning.
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?? Today’s Task:
??- Train a classifier on an imbalanced dataset (e.g., Fraud Detection).
??- Apply class weighting or oversampling techniques to balance the dataset.
??- Tune the model using these techniques and evaluate its performance with and without handling the imbalance.
Happy Learning & See You Soon!
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